Very recently, Camille Desjardins (from CNES), who is handling the validation of the L2A products generated by THEIA, has set up a systematic validation of the products delivered by MAJA, with the help of an operational service from CNES (OT/PE) (Bruno Besson, and Nicolas Guilleminot from Thales Services, using tools developed by Aurélie Courtois, also from Thales)

Systematically, a comparison of AOT and water vapour is made for every Sentinel-2 L2A product from THEIA which observes one of the sites of the Aeronet network.

Both plots below show the results obtained during the month of February, for the Aerosol Optical Thickness (left), and for the water vapour content (right). Blue dots correspond to validations in ideal conditions (low cloud amount, no gap filling, and quality assured Aeronet data (Level 2.0). The red dots allow degraded conditions, and most of them correspond to the unavailability, yet, of version 2.0 Aeronet data. As data are processed in near real time, and level 2.0 data are made available a few months later, these plots rely mainly on Level 1.5 data, which are more prone to errors (such as a calibration drift... or the presence of a spider in the instrument tubes).

Aerosol optical thickness validation of Sentinel-2 L2A for all Aeronet match-ups gathered in February 2018

Water vapour validation of Sentinel-2 L2A for all Aeronet match-ups gathered in February 2018 (in g/cm2)

The results are pretty good ! The water vapour results are as usual, with a very accurate determination for low water vapour content, and an overestimation when there is too much water vapour. This is due to the very simple model we use, which supposes the water vapour is above the over atmospheric layers and not mixed. But as we use the same model to perform the atmospheric correction, these errors should be really small.

Regarding AOT, we are getting much enhanced results compared to what we had usually. It is probably related to the continuous improvements we bring to MAJA and its parameters, and to the increased repetitivity of observations due to the availability of S2A and S2B working at full capacity. As our multi-temporal methods assume that the surface reflectance does not change from one image to the next one, the more frequent observations, the better results.

But it is probably not the only reason. Our aerosol estimates are usually good when the aerosol type present in the atmosphere is the same as the one we specified in MAJA processor: a continental model. When the aerosol model is wrong, often in the case of dust, our AOT estimates are too low. These cases correspond to the points in the lower part of the AOT diagram. They are probably much less frequent in winter, as the ground is more wet, and the dust is less easily blown by the wind.

In a few months, the version 3 of MAJA will be installed in MUSCATE, and this version will use an aerosol type variable with time and location, thanks to the use of aerosol forecasts from Copernicus Atmosphere Monitoring Service. This was explained in Bastien Rouquié's post, and should significantly improve our results.

]]>http://www.cesbio.ups-tlse.fr/multitemp/?feed=rss2&p=129390Sentinel-2 goes globalhttp://www.cesbio.ups-tlse.fr/multitemp/?p=12935&utm_source=rss&utm_medium=rss&utm_campaign=sentinel-2-goes-global
http://www.cesbio.ups-tlse.fr/multitemp/?p=12935#commentsMon, 05 Mar 2018 09:53:45 +0000Olivier Hagollehttp://www.cesbio.ups-tlse.fr/multitemp/?p=12935Continue reading]]> Great news ! As announced in Sentinel-2 Mission status, laser links to geostationary relay satellites are now working, both for S2B (since last October) and S2B (since a few days ago). Sentinel-2 5 days repetitivity is now nominal above all lands, and that's cool ! A big thank you to ESA, Copernicus and all the engineers who strived to achieve that !

Map of S2A and S2B acquisition segments on the 28th of February. Almost all segments over continents were acquires, and are available on https://peps.cnes.fr

MUSCATE is in a good shape these days thznkd to the continuous efforts of the development team (CNES and CAP GEMINI) who solved several issues. The counter of Sentinel-2 Level 2A products reached 70 000 products this night, just one month after reaching 60000. If we sum all the products delivered by MUSCATE, we are reaching 99 000 images. MUSCATE also distributes Sentinel-2 Snow masks over mountains, and Spot World Heritage data (old SPOT data reprocessed after ortho-rectification.and made available for free).

This good shape allows us to increase our production rhythm. We have started processing the Sentinel-2B data acquired between July and October 2017, as we had started processing S2B in November 2017 only. But since MAJA is a multi-temporal processor, we are in fact starting a complete reprocessing of the data, including S2A and S2B. The quality of S2A products should therefore also benefit from the improved repetitivity of observations.

This reprocessing will last several weeks. We are starting with data from France and will go on with our neighbouring European countries, then sites in Maghreb, the remaining sites of Africa, and finally, the rest of the sites in the world.

In case you have an urgent need for some tiles, please ask ! (of course, it is only applicable to the tiles already in our list)

Atmospheric absorption : in blue, the surface reflectance of a vegetation pixel, as a function of wavelength. In red, the reflectance of the same pixel at the top of atmosphere. For a wavelength of 1.38 µm, water vapour totally absorbs the light that comes from the earth surface at sea level.

Anyway, I recently found some time to work on improving cloud masks above water. And these last days, i worked at improving the detection of high cloud using the cirrus band, which is available on Sentinel-2 as well as Landsat 8. As explained in this post, the "cirrus" band is locates in a strong absorption band of water vapour, strong enough to prevent photons from going from the sun to the satellite via the earth surface at sea level. As most of the water vapour lies in the low layers of the atmosphere, the clouds situated higher in the atmosphere have more chances to reflect light to the satellite. As a result, this channel allows us to detect the high clouds.

The detection method looks simple : just use a threshold on the cirrus band. Well that's not that easy !

Mountains can also be above the absorbing layer and send light to the satellite. We have to account for earth surface altitude to detect cirrus clouds. Even if theory suggests an exponential law, we have been using so far a threshold that varies linearly with the altitude. We therefore need to determine the offset and gain of the linear law :

Threshold= S0 + G*h

To tune these values, one needs two steps. First determining the value of S0 using images in low altitude, an then determining the gain on images with higher altitude. Of course, you need to process hundred of images on different sites and different dates to accumulate different surface reflectance and atmospheric conditions.

The plot provided below shows our new threshold law, and compares it to samples of cirrus and cloud free observations from 6 different images, with and without clouds, from three sites, one at law altitude, near Toulouse (S2 tilename = 31TCJ), and two in altitude, on the high plateaux of Madagascar (S2 tilename is 38KPC) and on Atlas mountains in Morocco (S2 tilename is 29RPQ). The data set in this plot is just illustrative of the complexity of the detection.

Sentinel-2 cirrus band reflectance as a function of surface altitude for cirrus clouds (blue) and cloud free (red) pixels for 6 images from three sites in France(31TFJ), Madagascar (38KPC) and Morocco (29RPQ). The New Threshold line is in green, while the previous one (until product version 1.5 is in black).

The new thresholding will be included in version 1.6 of the products delivered by Theia, in a couple of weeks. The previous threshold law had been set to prevent classifying as clouds dry altitude regions, but had the drawback to miss detection of thin cirrus clouds. On the contrary, the new threshold will detect more clouds, but also sometimes classify high altitude regions with a dry atmosphere as clouds.

This is what we observe in the following example, over Atlas, on a dry day :

Old cirrus threshold

New cirrus threshold

We still can hope that these bright regions with a dry air are not often cloudy, and that only the driest dates will be wrongly classified as clouds.

This case excepted, the new threshold improves the detection of thin clouds. For instance for this other date over Atlas.

Old cirrus threshold

New cirrus threshold

Or maybe over that date in Madagascar :

Old cirrus threshold

New cirrus threshold

Really good observers will notice that we are still missing the thinnest parts of the cirrus clouds, but that's the price to pay not to declare as cloudy the dry mountains. MAJA cloud detection does not only rely on the cirrus bands and several other tests can also detect the thin clouds.

Some of you might also have noticed on the above plot the detection threshold of cirrus clouds could be lower to discard more of the blue cloud markers. However, some of the blue symbols were sampled on the image below (left, the cirrus band reflectance coded between 0 and 0.02). Cirrus clouds are present, but they do not disturb the values in the other channels. It would therefore be a pity to advise users not to use these pixels by classifying them as clouds. As I said, high cloud detection with the cirrus band is not that easy !

Sentinel-2 cirrus band reflectance as a function of surface altitude for cirrus clouds (blue) and cloud free (red) pixels for 6 images from three sites in France(31TFJ), Madagascar (38KPC) and Morocco (29RPQ). The New Threshold line is in green, while the previous one (until product version 1.5 is in black).

]]>http://www.cesbio.ups-tlse.fr/multitemp/?feed=rss2&p=128810Another validation of CESBIO's 2016 France land-cover maphttp://www.cesbio.ups-tlse.fr/multitemp/?p=12869&utm_source=rss&utm_medium=rss&utm_campaign=another-validation-of-cesbios-2016-france-land-cover-map
http://www.cesbio.ups-tlse.fr/multitemp/?p=12869#commentsWed, 21 Feb 2018 09:27:23 +0000Jordi Ingladahttp://www.cesbio.ups-tlse.fr/multitemp/?p=12869Continue reading]]>In this post, a validation of the land-cover map of France produced by CESBIO for the 2016 period was presented. This validation used independent data (that is data collected by different teams and using different procedures than the data used for the classifier training), but the validation procedure consisted in applying classical machine learning metrics which, as described in this other post, have some limitations.

A fully independent validation following a sound protocol is costly and needs skills and expertise that are very specific. SIRS is a company which is specialised in the production of geographic data from satellite or aerial images. Among other things, they are the producers of Corine Land Cover for France and they are also responsible for quality control and validation of other Copernicus Land products.

SIRS has recently performed a validation of the 2016 France land-cover map. The executive summary of the report reads as follows:

This report provides the evaluation results of the CESBIO OSO 2016 10m layer and the CESBIO OSO 2016 20m layer.

The thematic accuracy assessment was conducted in a two-stage process:

An initial blind interpretation in which the validation team did not have knowledge of the product’s thematic classes.

A plausibility analysis was performed on all sample units in disagreement with the production data to consider the following cases:

Uncertain code, both producer and operator codes are plausible. Final validation code used is producer code.

Error from first validation interpretation. Final validation used is producer code

Error from producer. Final validation code used is from first validation interpretation

Producer and operator are both wrong. Final Validation code used is a new code from this second interpretation.

Resulting to this two-stage approach, it should be noticed that the plausibility analysis exhibit better results than the blind analysis.

The thematic accuracy assessment was carried out over 1,428 sample units covering France and Corsica.
The final results show that the CESBIO OSO product meet the usually accepted thematic validation requirement, i.e. 85 % in both blind interpretation and plausibility analysis. Indeed, the overall accuracies obtained are 81.4 +/- 3.68% for the blind analysis and 91.7 +/- 1.25% for the plausibility analysis on the CESBIO OSO 10m layer. The analysis on the 20m layer shows us that the overall accuracy for the blind approach is 81.1 +/-3.65% and 88.2 +/-3.15% for the plausibility approach.
Quality checks of the validation points have been made by French experts. It should be noticed that for the blind analysis, the methodology of control was based mostly on Google Earth imagery, no additional thematic source of information that could provide further context was used such as forest stand maps, peatland maps, etc.

These results are very good news for us and for our users. The report also contains interesting recommendations that will help us to improve our algorithms. The full report is available for download.

Since we understood the bugs which were slowing MUSCATE, and we found ways to mitigate them, the production rhythm of MUSCATE improved and we were able to extend the zones where we provide Sentinel-2 (A&B) L2A products. L2A products provide surface reflectances after correction of atmospheric effects and with a high quality cloud mask. The products we deliver are provided by MAJA processor.

We just released all the data acquired on the Maghreb coastal zone, from Morocco to Tunisia. A few missing tiles have also been added in South Morocco, and on Cap Bon in Tunisia. With these new tiles, we now monitor all the lands that surround the occidental part of Mediterranean sea, adding 50 tiles to those already processed.

The processing started with the images acquired on the 1st of November 2017, to allow the monitoring of 2017-2018 crop season. The processing will go on in real time. We will also try to provide the data acquired before that date, but a little later.

As an example, here is the amazing time already available on Gibraltar strait. Detected clouds are circled in green, while cloud shadows are circled in yellow. We see that MAJA processor is able to detect the thinnest clouds and has a very little rate of commission errors. For the shadows, omissions and commissions are more frequent: the detection of cloud shadows is even more difficult than the detection of clouds.

ESA too is producing L2A products on the same tile with the Sen2cor processor. But strangely, only six images are available between beginning of November 2017 and mid February, while Theia produced about 30 images. The image below compares the surface reflectances and the cloud and shadow masks. Sen2cor tends to classify every bright pixel as a cloud, which is not the case for MAJA. Sen2cor water mask is of better quality, as MAJA's one in processed at 240m resolution and is not aimed at being used.